Learning analytics in practice: setting up a laboratory for action research at the Universitat Oberta de Catalunya

Since its inception in 1994 as a purely online university, the Universitat Oberta de Catalunya(UOC) has been able to position itself among the main universities of the Catalan and Spanish university systems. Most of the students at the UOC (currently more than 60,000) are adults who have a profile that could hardly fit into the traditional university system, thus finding in the UOC an opportunity to start or continue their higher education grades, in a very innovative environment. The intensive use of ICT for both theteaching/learning processes and management allowsresearchers and practitioners to obtain data aboutwhat takes place in the UOC Virtual Campus, which is continuously being improved according to suchfindings.

A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning

Peer-reviewed

DAI-DEPUR: an integrated and distributed architecture for wastewater treatment plants supervision

The activated sludge process - the main biological technology usually applied towastewater treatment plants (WWTP) - directly depends on live beings (microorganisms), and therefore on unforeseen changes produced by them. It could be possible to get a good plant operation if the supervisory control system is able to react to the changes and deviations in the system and can take thenecessary actions to restore the system’s performance. These decisions are oftenbased both on physical, chemical, microbiological principles (suitable to bemodelled by conventional control algorithms) and on some knowledge (suitable to be modelled by knowledge-based systems). But one of the key problems in knowledge-based control systems design is the development of an architecture able to manage efficiently the different elements of the process (integrated architecture), to learn from previous cases (spec@c experimental knowledge) and to acquire the domain knowledge (general expert knowledge). These problems increase when the process belongs to an ill-structured domain and is composed of several complex operational units. Therefore, an integrated and distributed AIarchitecture seems to be a good choice. This paper proposes an integrated and distributed supervisory multi-level architecture for the supervision of WWTP, that overcomes some of the main troubles of classical control techniques and those of knowledge-based systems applied to real world systems

Design and evaluation of learning algorithms for dynamic resource management in virtual networks

Network virtualisation is considerably gaining attentionas a solution to ossification of the Internet. However, thesuccess of network virtualisation will depend in part on how efficientlythe virtual networks utilise substrate network resources.In this paper, we propose a machine learning-based approachto virtual network resource management. We propose to modelthe substrate network as a decentralised system and introducea learning algorithm in each substrate node and substrate link,providing self-organization capabilities. We propose a multiagentlearning algorithm that carries out the substrate network resourcemanagement in a coordinated and decentralised way. The taskof these agents is to use evaluative feedback to learn an optimalpolicy so as to dynamically allocate network resources to virtualnodes and links. The agents ensure that while the virtual networkshave the resources they need at any given time, only the requiredresources are reserved for this purpose. Simulations show thatour dynamic approach significantly improves the virtual networkacceptance ratio and the maximum number of accepted virtualnetwork requests at any time while ensuring that virtual networkquality of service requirements such as packet drop rate andvirtual link delay are not affected.

Characterization and Control Strategies of an Integrated Chemical-Biological System for the Remediation of Toxic Pollutants in Wastewater: A case of study

In a previous work, a hybrid system consisting of an advanced oxidation process (AOP) named Photo-Fenton (Ph-F) and a fixed bed biological treatment operating as a sequencing batch biofilm reactor (SBBR) was started-up and optimized to treat 200 mg·L-1 of 4-chlorophenol (4-CP) as a model compound. In this work, studies of reactor stability and control as well as microbial population determination by molecular biology techniques were carried out to further characterize and control the biological reactor. Results revealed that the integrated system was flexible and even able to overcome toxic shock loads. Oxygen uptake rate (OUR) in situ was shown to be a valid tool to control the SBBR operation, to detect toxic conditions to the biomass, and to assess the recovery of performance. A microbial characterization by 16S rDNA sequence analysis reveals that the biological population was varied, although about 30% of the bacteria belonged to the Wautersia genus.